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This thesis presents real-time guidance strategies for unmanned aerial vehicles (UAVs) that can be used to enhance their flight endurance by utilizing in-situ measurements of wind speeds and wind gradients. In these strategies, periodic adjustments can be made in the airspeed and/or heading angle command for the UAV to minimize a projected power requirement at some future time. In this thesis, UAV flights are described by a three-dimensional dynamic point-mass model. A stochastic wind field model has been developed not only to reflect the mean wind magnitude behaviour in both vertical and horizontal axis, but also has it been extended to characterize wind direction behaviour as well. Proposed wind field model is assumed that it is consisted of a constant term plus terms that vary sinusoidally with respect to the location and time. Onboard closed-loop trajectory tracking logics that follow airspeed vector commands are modeled using the method of feedback linearization. To evaluate the benefits of these strategies in enhancing UAV flight endurance, a reference strategy is introduced in which the UAV would follow the optimal airspeed command in a steady level flight under zero wind conditions. A performance measure is defined as the average power consumption both over a specified time interval and over different initial heading angles of the UAV. A relative benefit criterion is then defined as the percentage improvement in the performance measure of a proposed strategy over that of the reference strategy. Extensive numerical simulations are conducted to show efficiency and applicability of the proposed algorithms. Results demonstrate the efficiency, benefits and trends of power savings of the proposed real-time guidance strategies in level flights.